Overview

Dataset statistics

Number of variables29
Number of observations5446331
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 GiB
Average record size in memory264.8 B

Variable types

Numeric18
Categorical11

Alerts

No_Inspections is highly overall correlated with gas_naturalHigh correlation
preventive_maintenance_rate is highly overall correlated with No_Incidents and 1 other fieldsHigh correlation
Average_MonthsLastRev is highly overall correlated with MonthsLastRevHigh correlation
MonthsLastRev is highly overall correlated with Average_MonthsLastRevHigh correlation
NumConnections is highly overall correlated with area_connection and 2 other fieldsHigh correlation
Relative_Thickness is highly overall correlated with Diameter2 and 1 other fieldsHigh correlation
pipe_area is highly overall correlated with Length2High correlation
area_connection is highly overall correlated with NumConnectionsHigh correlation
Diameter2 is highly overall correlated with Relative_ThicknessHigh correlation
Length2 is highly overall correlated with NumConnections and 1 other fieldsHigh correlation
Pressure2 is highly overall correlated with Relative_Thickness and 2 other fieldsHigh correlation
Average year Humidity (%) is highly overall correlated with Yearly Sun Hours (hours)High correlation
Yearly Sun Hours (hours) is highly overall correlated with Average year Humidity (%)High correlation
No_Incidents is highly overall correlated with preventive_maintenance_rate and 1 other fieldsHigh correlation
Incidence is highly overall correlated with preventive_maintenance_rate and 1 other fieldsHigh correlation
connection_bool is highly overall correlated with NumConnectionsHigh correlation
gas_natural is highly overall correlated with No_InspectionsHigh correlation
Material_Acrylonitrile-Butadiene-Styrene is highly overall correlated with Pressure2 and 1 other fieldsHigh correlation
Material_Polyethylene is highly overall correlated with Pressure2 and 1 other fieldsHigh correlation
No_Incidents is highly imbalanced (97.7%)Imbalance
Incidence is highly imbalanced (98.5%)Imbalance
NumConnectionsUnder is highly imbalanced (99.9%)Imbalance
gas_natural is highly imbalanced (87.0%)Imbalance
Material_Acrylonitrile-Butadiene-Styrene is highly imbalanced (67.5%)Imbalance
Material_Copper is highly imbalanced (97.6%)Imbalance
Material_Fiberglass-Reinforced Plastic is highly imbalanced (85.9%)Imbalance
Material_Polyethylene is highly imbalanced (57.8%)Imbalance
Material_Polypropylene is highly imbalanced (96.7%)Imbalance
area_connection is highly skewed (γ1 = 474.6354216)Skewed
incidence_area is highly skewed (γ1 = 461.7287922)Skewed
preventive_maintenance_rate has 5414855 (99.4%) zerosZeros
NumConnections has 3382310 (62.1%) zerosZeros
area_connection has 3382310 (62.1%) zerosZeros
incidence_area has 5438615 (99.9%) zerosZeros

Reproduction

Analysis started2023-02-18 21:40:13.692756
Analysis finished2023-02-18 21:52:23.260588
Duration12 minutes and 9.57 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

PipeId
Real number (ℝ)

Distinct1227387
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0300577 × 108
Minimum489616
Maximum4.5199531 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 MiB
2023-02-18T22:52:23.360399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum489616
5-th percentile12068423
Q11.3355351 × 108
median1.9027323 × 108
Q32.9829521 × 108
95-th percentile3.9716302 × 108
Maximum4.5199531 × 108
Range4.5150569 × 108
Interquartile range (IQR)1.647417 × 108

Descriptive statistics

Standard deviation1.1416027 × 108
Coefficient of variation (CV)0.56234989
Kurtosis-0.74522921
Mean2.0300577 × 108
Median Absolute Deviation (MAD)93833945
Skewness-0.082278506
Sum1.1056366 × 1015
Variance1.3032568 × 1016
MonotonicityNot monotonic
2023-02-18T22:52:23.507042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310826986 11
 
< 0.1%
263765118 11
 
< 0.1%
190443521 11
 
< 0.1%
190443535 11
 
< 0.1%
190576614 11
 
< 0.1%
190576621 11
 
< 0.1%
190948042 11
 
< 0.1%
263764681 11
 
< 0.1%
263765023 11
 
< 0.1%
263765060 11
 
< 0.1%
Other values (1227377) 5446221
> 99.9%
ValueCountFrequency (%)
489616 5
< 0.1%
489645 5
< 0.1%
489780 5
< 0.1%
489790 5
< 0.1%
489792 5
< 0.1%
489793 5
< 0.1%
489981 5
< 0.1%
489982 5
< 0.1%
489996 5
< 0.1%
490308 4
< 0.1%
ValueCountFrequency (%)
451995309 4
< 0.1%
451995260 4
< 0.1%
451995254 2
< 0.1%
451195406 3
< 0.1%
451195391 4
< 0.1%
451195364 3
< 0.1%
451195284 4
< 0.1%
451194879 4
< 0.1%
451194601 4
< 0.1%
451194531 4
< 0.1%

No_Inspections
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8989902
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 MiB
2023-02-18T22:52:23.601834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median5
Q35
95-th percentile6
Maximum11
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0369869
Coefficient of variation (CV)0.2116736
Kurtosis4.6942796
Mean4.8989902
Median Absolute Deviation (MAD)0
Skewness-0.87722946
Sum26681522
Variance1.0753418
MonotonicityNot monotonic
2023-02-18T22:52:23.681857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5 3409988
62.6%
6 1033936
 
19.0%
4 424427
 
7.8%
3 268566
 
4.9%
2 198548
 
3.6%
1 53739
 
1.0%
7 27636
 
0.5%
10 15590
 
0.3%
8 5834
 
0.1%
9 5518
 
0.1%
ValueCountFrequency (%)
1 53739
 
1.0%
2 198548
 
3.6%
3 268566
 
4.9%
4 424427
 
7.8%
5 3409988
62.6%
6 1033936
 
19.0%
7 27636
 
0.5%
8 5834
 
0.1%
9 5518
 
0.1%
10 15590
 
0.3%
ValueCountFrequency (%)
11 2549
 
< 0.1%
10 15590
 
0.3%
9 5518
 
0.1%
8 5834
 
0.1%
7 27636
 
0.5%
6 1033936
 
19.0%
5 3409988
62.6%
4 424427
 
7.8%
3 268566
 
4.9%
2 198548
 
3.6%

No_Incidents
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size83.1 MiB
0.0
5414855 
1.0
 
29505
2.0
 
1865
3.0
 
94
4.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters16338993
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5414855
99.4%
1.0 29505
 
0.5%
2.0 1865
 
< 0.1%
3.0 94
 
< 0.1%
4.0 12
 
< 0.1%

Length

2023-02-18T22:52:23.768687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T22:52:23.871664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5414855
99.4%
1.0 29505
 
0.5%
2.0 1865
 
< 0.1%
3.0 94
 
< 0.1%
4.0 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 10861186
66.5%
. 5446331
33.3%
1 29505
 
0.2%
2 1865
 
< 0.1%
3 94
 
< 0.1%
4 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10892662
66.7%
Other Punctuation 5446331
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10861186
99.7%
1 29505
 
0.3%
2 1865
 
< 0.1%
3 94
 
< 0.1%
4 12
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 5446331
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16338993
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10861186
66.5%
. 5446331
33.3%
1 29505
 
0.2%
2 1865
 
< 0.1%
3 94
 
< 0.1%
4 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16338993
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10861186
66.5%
. 5446331
33.3%
1 29505
 
0.2%
2 1865
 
< 0.1%
3 94
 
< 0.1%
4 12
 
< 0.1%

InspectionYear
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.5865
Minimum2010
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 MiB
2023-02-18T22:52:23.954363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2011
Q12013
median2016
Q32018
95-th percentile2020
Maximum2021
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9048527
Coefficient of variation (CV)0.0014411948
Kurtosis-1.1783153
Mean2015.5865
Median Absolute Deviation (MAD)2
Skewness-0.094194503
Sum1.0977551 × 1010
Variance8.4381691
MonotonicityNot monotonic
2023-02-18T22:52:24.038749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2019 590240
10.8%
2017 589947
10.8%
2018 556874
10.2%
2020 553505
10.2%
2016 539475
9.9%
2015 534623
9.8%
2012 528261
9.7%
2014 512780
9.4%
2013 503194
9.2%
2011 454474
8.3%
Other values (2) 82958
 
1.5%
ValueCountFrequency (%)
2010 81842
 
1.5%
2011 454474
8.3%
2012 528261
9.7%
2013 503194
9.2%
2014 512780
9.4%
2015 534623
9.8%
2016 539475
9.9%
2017 589947
10.8%
2018 556874
10.2%
2019 590240
10.8%
ValueCountFrequency (%)
2021 1116
 
< 0.1%
2020 553505
10.2%
2019 590240
10.8%
2018 556874
10.2%
2017 589947
10.8%
2016 539475
9.9%
2015 534623
9.8%
2014 512780
9.4%
2013 503194
9.2%
2012 528261
9.7%

preventive_maintenance_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0050959271
Minimum0
Maximum3
Zeros5414855
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size83.1 MiB
2023-02-18T22:52:24.145056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.074123837
Coefficient of variation (CV)14.545702
Kurtosis478.49656
Mean0.0050959271
Median Absolute Deviation (MAD)0
Skewness19.133955
Sum27754.106
Variance0.0054943432
MonotonicityNot monotonic
2023-02-18T22:52:24.251594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 5414855
99.4%
0.76 9999
 
0.2%
0.6388888889 6937
 
0.1%
0.72 2960
 
0.1%
1.222222222 2108
 
< 0.1%
1.75 2047
 
< 0.1%
0.68 1332
 
< 0.1%
0.6111111111 992
 
< 0.1%
0.9375 978
 
< 0.1%
0.5833333333 952
 
< 0.1%
Other values (34) 3171
 
0.1%
ValueCountFrequency (%)
0 5414855
99.4%
0.38 10
 
< 0.1%
0.39 30
 
< 0.1%
0.4197530864 9
 
< 0.1%
0.4320987654 18
 
< 0.1%
0.46875 16
 
< 0.1%
0.484375 56
 
< 0.1%
0.5102040816 49
 
< 0.1%
0.5306122449 84
 
< 0.1%
0.5510204082 427
 
< 0.1%
ValueCountFrequency (%)
3 494
 
< 0.1%
2.666666667 2
 
< 0.1%
2.5 8
 
< 0.1%
2.4375 4
 
< 0.1%
2.222222222 107
 
< 0.1%
2.04 30
 
< 0.1%
2 73
 
< 0.1%
1.92 10
 
< 0.1%
1.777777778 6
 
< 0.1%
1.75 2047
< 0.1%

Age_pipe_at_inspection
Real number (ℝ)

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.992784
Minimum0
Maximum34
Zeros46811
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size83.1 MiB
2023-02-18T22:52:24.368162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median12
Q318
95-th percentile27
Maximum34
Range34
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.3800932
Coefficient of variation (CV)0.56801476
Kurtosis-0.39978255
Mean12.992784
Median Absolute Deviation (MAD)5
Skewness0.42033395
Sum70763003
Variance54.465775
MonotonicityNot monotonic
2023-02-18T22:52:24.493699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
12 289568
 
5.3%
11 285862
 
5.2%
13 280207
 
5.1%
10 279648
 
5.1%
14 265808
 
4.9%
9 261922
 
4.8%
15 255356
 
4.7%
8 252612
 
4.6%
16 237944
 
4.4%
7 224358
 
4.1%
Other values (25) 2813046
51.7%
ValueCountFrequency (%)
0 46811
 
0.9%
1 142944
2.6%
2 167313
3.1%
3 184655
3.4%
4 209132
3.8%
5 195788
3.6%
6 220917
4.1%
7 224358
4.1%
8 252612
4.6%
9 261922
4.8%
ValueCountFrequency (%)
34 10234
 
0.2%
33 14484
 
0.3%
32 23183
 
0.4%
31 28597
 
0.5%
30 37852
0.7%
29 42860
0.8%
28 55513
1.0%
27 63538
1.2%
26 76847
1.4%
25 84059
1.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size83.1 MiB
1
4692360 
0
753971 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5446331
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4692360
86.2%
0 753971
 
13.8%

Length

2023-02-18T22:52:24.595288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T22:52:24.684900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 4692360
86.2%
0 753971
 
13.8%

Most occurring characters

ValueCountFrequency (%)
1 4692360
86.2%
0 753971
 
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5446331
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4692360
86.2%
0 753971
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5446331
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4692360
86.2%
0 753971
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5446331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4692360
86.2%
0 753971
 
13.8%

Average_MonthsLastRev
Real number (ℝ)

Distinct521
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.125285
Minimum0
Maximum77.5
Zeros626
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size83.1 MiB
2023-02-18T22:52:24.777454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q122.166667
median23.75
Q324
95-th percentile24.333333
Maximum77.5
Range77.5
Interquartile range (IQR)1.8333333

Descriptive statistics

Standard deviation2.2815171
Coefficient of variation (CV)0.098658983
Kurtosis23.116717
Mean23.125285
Median Absolute Deviation (MAD)0.45
Skewness0.45519165
Sum1.2594796 × 108
Variance5.2053203
MonotonicityNot monotonic
2023-02-18T22:52:24.900547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 1769271
32.5%
24.2 401542
 
7.4%
23.6 297025
 
5.5%
22 256109
 
4.7%
23.8 183863
 
3.4%
23.4 159475
 
2.9%
23 154310
 
2.8%
21 109728
 
2.0%
23.2 109638
 
2.0%
22.2 102293
 
1.9%
Other values (511) 1903077
34.9%
ValueCountFrequency (%)
0 626
< 0.1%
1 80
 
< 0.1%
2 117
 
< 0.1%
3 246
 
< 0.1%
4 283
< 0.1%
5 355
< 0.1%
6 353
< 0.1%
6.5 16
 
< 0.1%
7 323
< 0.1%
7.5 18
 
< 0.1%
ValueCountFrequency (%)
77.5 1
 
< 0.1%
76 1
 
< 0.1%
75 1
 
< 0.1%
70 2
 
< 0.1%
69 1
 
< 0.1%
68.5 2
 
< 0.1%
67.5 3
 
< 0.1%
67 4
 
< 0.1%
66.5 5
 
< 0.1%
66 20
< 0.1%

MonthsLastRev
Real number (ℝ)

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.992876
Minimum0
Maximum39
Zeros991
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size83.1 MiB
2023-02-18T22:52:25.023954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q123
median24
Q324
95-th percentile25
Maximum39
Range39
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.0592022
Coefficient of variation (CV)0.13305
Kurtosis14.436309
Mean22.992876
Median Absolute Deviation (MAD)0
Skewness-2.9752117
Sum1.2522681 × 108
Variance9.358718
MonotonicityNot monotonic
2023-02-18T22:52:25.135389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
24 2945518
54.1%
23 768204
 
14.1%
22 476271
 
8.7%
25 451794
 
8.3%
21 247992
 
4.6%
20 79017
 
1.5%
26 45677
 
0.8%
19 41509
 
0.8%
18 39943
 
0.7%
17 34166
 
0.6%
Other values (30) 316240
 
5.8%
ValueCountFrequency (%)
0 991
 
< 0.1%
1 1675
 
< 0.1%
2 2420
 
< 0.1%
3 4453
 
0.1%
4 4661
 
0.1%
5 8002
0.1%
6 9088
0.2%
7 9475
0.2%
8 9415
0.2%
9 16787
0.3%
ValueCountFrequency (%)
39 1037
 
< 0.1%
38 428
 
< 0.1%
37 899
 
< 0.1%
36 5661
0.1%
35 2728
 
0.1%
34 3650
 
0.1%
33 3488
 
0.1%
32 7881
0.1%
31 4343
 
0.1%
30 12355
0.2%

Incidence
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size83.1 MiB
0.0
5438615 
1.0
 
7716

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters16338993
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5438615
99.9%
1.0 7716
 
0.1%

Length

2023-02-18T22:52:25.243490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T22:52:25.331514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5438615
99.9%
1.0 7716
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 10884946
66.6%
. 5446331
33.3%
1 7716
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10892662
66.7%
Other Punctuation 5446331
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10884946
99.9%
1 7716
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 5446331
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16338993
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10884946
66.6%
. 5446331
33.3%
1 7716
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16338993
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10884946
66.6%
. 5446331
33.3%
1 7716
 
< 0.1%

NumConnections
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.78585106
Minimum0
Maximum34
Zeros3382310
Zeros (%)62.1%
Negative0
Negative (%)0.0%
Memory size83.1 MiB
2023-02-18T22:52:25.411675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum34
Range34
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5105416
Coefficient of variation (CV)1.9221729
Kurtosis22.226989
Mean0.78585106
Median Absolute Deviation (MAD)0
Skewness3.6867653
Sum4280005
Variance2.2817359
MonotonicityNot monotonic
2023-02-18T22:52:25.513120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 3382310
62.1%
1 1124787
 
20.7%
2 447037
 
8.2%
3 204385
 
3.8%
4 113491
 
2.1%
5 64384
 
1.2%
6 39757
 
0.7%
7 23239
 
0.4%
8 15871
 
0.3%
9 10236
 
0.2%
Other values (24) 20834
 
0.4%
ValueCountFrequency (%)
0 3382310
62.1%
1 1124787
 
20.7%
2 447037
 
8.2%
3 204385
 
3.8%
4 113491
 
2.1%
5 64384
 
1.2%
6 39757
 
0.7%
7 23239
 
0.4%
8 15871
 
0.3%
9 10236
 
0.2%
ValueCountFrequency (%)
34 5
 
< 0.1%
33 10
 
< 0.1%
32 2
 
< 0.1%
30 10
 
< 0.1%
29 17
 
< 0.1%
28 38
< 0.1%
27 19
< 0.1%
26 29
< 0.1%
25 17
 
< 0.1%
24 44
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size83.1 MiB
0
5445500 
1
 
811
2
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5446331
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5445500
> 99.9%
1 811
 
< 0.1%
2 20
 
< 0.1%

Length

2023-02-18T22:52:25.619492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T22:52:25.709642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5445500
> 99.9%
1 811
 
< 0.1%
2 20
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 5445500
> 99.9%
1 811
 
< 0.1%
2 20
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5446331
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5445500
> 99.9%
1 811
 
< 0.1%
2 20
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5446331
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5445500
> 99.9%
1 811
 
< 0.1%
2 20
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5446331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5445500
> 99.9%
1 811
 
< 0.1%
2 20
 
< 0.1%

Relative_Thickness
Real number (ℝ)

Distinct251
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5627146
Minimum0.0015875
Maximum24.384
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 MiB
2023-02-18T22:52:25.802460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0015875
5-th percentile0.0126
Q10.0225
median0.6
Q31.1
95-th percentile8
Maximum24.384
Range24.382413
Interquartile range (IQR)1.0775

Descriptive statistics

Standard deviation2.5425542
Coefficient of variation (CV)1.6270112
Kurtosis3.7104332
Mean1.5627146
Median Absolute Deviation (MAD)0.5775
Skewness2.0616556
Sum8511061.1
Variance6.464582
MonotonicityNot monotonic
2023-02-18T22:52:25.921985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7333333333 629816
 
11.6%
0.01575 599884
 
11.0%
0.0225 481142
 
8.8%
4.4 340464
 
6.3%
6.4 305922
 
5.6%
0.6 282696
 
5.2%
1.066666667 246598
 
4.5%
0.0275 226677
 
4.2%
8 201639
 
3.7%
0.42 195813
 
3.6%
Other values (241) 1935680
35.5%
ValueCountFrequency (%)
0.0015875 337
 
< 0.1%
0.00238125 12
 
< 0.1%
0.0025 4
 
< 0.1%
0.00275 30
 
< 0.1%
0.003 368
 
< 0.1%
0.003175 35998
0.7%
0.0032 3
 
< 0.1%
0.0035 9
 
< 0.1%
0.00375 794
 
< 0.1%
0.00381 6
 
< 0.1%
ValueCountFrequency (%)
24.384 26
 
< 0.1%
20.32 32
 
< 0.1%
20 102
 
< 0.1%
18.288 8
 
< 0.1%
16.256 490
 
< 0.1%
16 540
 
< 0.1%
14.224 10
 
< 0.1%
14.2 11
 
< 0.1%
14 372
 
< 0.1%
12.6 30002
0.6%

pipe_area
Real number (ℝ)

Distinct401305
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4250864
Minimum0.000502656
Maximum189.60639
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 MiB
2023-02-18T22:52:26.040817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.000502656
5-th percentile0.28721764
Q11.1803519
median3.9221965
Q311.58509
95-th percentile30.748096
Maximum189.60639
Range189.60589
Interquartile range (IQR)10.404738

Descriptive statistics

Standard deviation11.016982
Coefficient of variation (CV)1.3076403
Kurtosis8.6745535
Mean8.4250864
Median Absolute Deviation (MAD)3.3147776
Skewness2.4531382
Sum45885809
Variance121.3739
MonotonicityNot monotonic
2023-02-18T22:52:26.155165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.691152 6940
 
0.1%
0.345576 6308
 
0.1%
0.518364 6243
 
0.1%
0.1983166416 5889
 
0.1%
0.251328 5511
 
0.1%
0.1979208 5381
 
0.1%
0.565488 5285
 
0.1%
0.3958416 4942
 
0.1%
0.282744 4544
 
0.1%
1.005312 4206
 
0.1%
Other values (401295) 5391082
99.0%
ValueCountFrequency (%)
0.000502656 1
 
< 0.1%
0.000518364 1
 
< 0.1%
0.0006031872 5
< 0.1%
0.000989604 7
< 0.1%
0.001005312 2
 
< 0.1%
0.0011875248 8
< 0.1%
0.0012534984 1
 
< 0.1%
0.00141372 2
 
< 0.1%
0.00143633952 1
 
< 0.1%
0.0015833664 3
 
< 0.1%
ValueCountFrequency (%)
189.6063922 5
< 0.1%
166.409509 4
< 0.1%
166.3345001 6
< 0.1%
166.3265205 5
< 0.1%
164.9856177 6
< 0.1%
164.1991421 4
< 0.1%
163.4761845 5
< 0.1%
162.9718697 4
< 0.1%
155.8460298 5
< 0.1%
154.4767194 4
< 0.1%

area_connection
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct294111
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19547282
Minimum0
Maximum1989.4321
Zeros3382310
Zeros (%)62.1%
Negative0
Negative (%)0.0%
Memory size83.1 MiB
2023-02-18T22:52:26.283398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.13434303
95-th percentile0.69440985
Maximum1989.4321
Range1989.4321
Interquartile range (IQR)0.13434303

Descriptive statistics

Standard deviation1.9343258
Coefficient of variation (CV)9.8956253
Kurtosis423577.85
Mean0.19547282
Median Absolute Deviation (MAD)0
Skewness474.63542
Sum1064609.7
Variance3.7416162
MonotonicityNot monotonic
2023-02-18T22:52:26.402903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3382310
62.1%
1.010505212 838
 
< 0.1%
2.52626303 788
 
< 0.1%
5.052526061 738
 
< 0.1%
0.8420876768 711
 
< 0.1%
5.042441179 682
 
< 0.1%
1.263131515 559
 
< 0.1%
1.684175354 558
 
< 0.1%
0.7217894373 528
 
< 0.1%
7.957728546 464
 
< 0.1%
Other values (294101) 2058155
37.8%
ValueCountFrequency (%)
0 3382310
62.1%
0.007715556516 5
 
< 0.1%
0.009769187457 5
 
< 0.1%
0.009792381384 5
 
< 0.1%
0.009840825945 5
 
< 0.1%
0.009911967005 5
 
< 0.1%
0.009919848549 5
 
< 0.1%
0.009930571972 5
 
< 0.1%
0.009956403025 5
 
< 0.1%
0.009956599227 6
 
< 0.1%
ValueCountFrequency (%)
1989.432136 1
 
< 0.1%
1929.146314 1
 
< 0.1%
1010.505212 2
< 0.1%
797.7672728 1
 
< 0.1%
530.5152364 2
< 0.1%
361.7149339 1
 
< 0.1%
360.8947186 3
< 0.1%
234.9144958 1
 
< 0.1%
226.0718337 1
 
< 0.1%
222.5938055 4
< 0.1%

incidence_area
Real number (ℝ)

SKEWED  ZEROS 

Distinct7141
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0011995106
Minimum0
Maximum142.1023
Zeros5438615
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size83.1 MiB
2023-02-18T22:52:26.529459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum142.1023
Range142.1023
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1394909
Coefficient of variation (CV)116.28985
Kurtosis349428.73
Mean0.0011995106
Median Absolute Deviation (MAD)0
Skewness461.72879
Sum6532.9315
Variance0.019457712
MonotonicityNot monotonic
2023-02-18T22:52:26.640053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5438615
99.9%
3.978864273 16
 
< 0.1%
7.957728546 10
 
< 0.1%
5.052526061 8
 
< 0.1%
3.9808547 6
 
< 0.1%
21.22060946 6
 
< 0.1%
15.91545709 6
 
< 0.1%
5.305152364 5
 
< 0.1%
1.989432136 5
 
< 0.1%
3.536768243 5
 
< 0.1%
Other values (7131) 7649
 
0.1%
ValueCountFrequency (%)
0 5438615
99.9%
0.009659922876 1
 
< 0.1%
0.009667593515 1
 
< 0.1%
0.009801689822 1
 
< 0.1%
0.01009112637 1
 
< 0.1%
0.01015348424 1
 
< 0.1%
0.01017956655 1
 
< 0.1%
0.01018410014 1
 
< 0.1%
0.01019055084 1
 
< 0.1%
0.01019826426 2
 
< 0.1%
ValueCountFrequency (%)
142.1022955 1
< 0.1%
117.5006061 1
< 0.1%
89.91783668 1
< 0.1%
52.94563238 1
< 0.1%
51.03561678 1
< 0.1%
50.04860721 1
< 0.1%
45.60302892 1
< 0.1%
45.47273455 1
< 0.1%
41.44650284 1
< 0.1%
39.78864273 2
< 0.1%

connection_bool
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size83.1 MiB
0
4507097 
1
939234 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5446331
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4507097
82.8%
1 939234
 
17.2%

Length

2023-02-18T22:52:26.746552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T22:52:26.835192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4507097
82.8%
1 939234
 
17.2%

Most occurring characters

ValueCountFrequency (%)
0 4507097
82.8%
1 939234
 
17.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5446331
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4507097
82.8%
1 939234
 
17.2%

Most occurring scripts

ValueCountFrequency (%)
Common 5446331
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4507097
82.8%
1 939234
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5446331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4507097
82.8%
1 939234
 
17.2%

gas_natural
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size83.1 MiB
1
5348700 
0
 
97631

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5446331
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5348700
98.2%
0 97631
 
1.8%

Length

2023-02-18T22:52:26.910673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T22:52:26.998562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 5348700
98.2%
0 97631
 
1.8%

Most occurring characters

ValueCountFrequency (%)
1 5348700
98.2%
0 97631
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5446331
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5348700
98.2%
0 97631
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5446331
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5348700
98.2%
0 97631
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5446331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5348700
98.2%
0 97631
 
1.8%

Material_Acrylonitrile-Butadiene-Styrene
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size83.1 MiB
0
5123338 
1
 
322993

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5446331
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5123338
94.1%
1 322993
 
5.9%

Length

2023-02-18T22:52:27.073446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T22:52:27.160285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5123338
94.1%
1 322993
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 5123338
94.1%
1 322993
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5446331
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5123338
94.1%
1 322993
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common 5446331
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5123338
94.1%
1 322993
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5446331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5123338
94.1%
1 322993
 
5.9%

Material_Copper
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size83.1 MiB
0
5433651 
1
 
12680

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5446331
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5433651
99.8%
1 12680
 
0.2%

Length

2023-02-18T22:52:27.232704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T22:52:27.319528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5433651
99.8%
1 12680
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 5433651
99.8%
1 12680
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5446331
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5433651
99.8%
1 12680
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 5446331
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5433651
99.8%
1 12680
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5446331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5433651
99.8%
1 12680
 
0.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size83.1 MiB
0
5337365 
1
 
108966

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5446331
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5337365
98.0%
1 108966
 
2.0%

Length

2023-02-18T22:52:27.391389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T22:52:27.479850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5337365
98.0%
1 108966
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 5337365
98.0%
1 108966
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5446331
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5337365
98.0%
1 108966
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5446331
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5337365
98.0%
1 108966
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5446331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5337365
98.0%
1 108966
 
2.0%

Material_Polyethylene
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size83.1 MiB
1
4978790 
0
 
467541

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5446331
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4978790
91.4%
0 467541
 
8.6%

Length

2023-02-18T22:52:27.552960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T22:52:27.640496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 4978790
91.4%
0 467541
 
8.6%

Most occurring characters

ValueCountFrequency (%)
1 4978790
91.4%
0 467541
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5446331
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4978790
91.4%
0 467541
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
Common 5446331
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4978790
91.4%
0 467541
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5446331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4978790
91.4%
0 467541
 
8.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size83.1 MiB
0
5427454 
1
 
18877

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5446331
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5427454
99.7%
1 18877
 
0.3%

Length

2023-02-18T22:52:27.715129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T22:52:27.802800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5427454
99.7%
1 18877
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 5427454
99.7%
1 18877
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5446331
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5427454
99.7%
1 18877
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5446331
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5427454
99.7%
1 18877
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5446331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5427454
99.7%
1 18877
 
0.3%

Diameter2
Real number (ℝ)

Distinct61
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.016325103
Minimum0.0001
Maximum0.37161216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 MiB
2023-02-18T22:52:27.892738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0001
5-th percentile0.003969
Q10.0081
median0.0121
Q30.0256
95-th percentile0.04
Maximum0.37161216
Range0.37151216
Interquartile range (IQR)0.0175

Descriptive statistics

Standard deviation0.016951005
Coefficient of variation (CV)1.0383399
Kurtosis33.168043
Mean0.016325103
Median Absolute Deviation (MAD)0.008131
Skewness3.9501492
Sum88911.912
Variance0.00028733656
MonotonicityNot monotonic
2023-02-18T22:52:28.011884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0121 1475195
27.1%
0.003969 1046095
19.2%
0.0081 1010190
18.5%
0.0256 749971
13.8%
0.04 517842
 
9.5%
0.0016 104193
 
1.9%
0.02322576 81206
 
1.5%
0.0625 67646
 
1.2%
0.01032256 67151
 
1.2%
0.00258064 49202
 
0.9%
Other values (51) 277640
 
5.1%
ValueCountFrequency (%)
0.0001 158
 
< 0.1%
0.000121 60
 
< 0.1%
0.000144 1292
 
< 0.1%
0.00016129 11
 
< 0.1%
0.000169 105
 
< 0.1%
0.000196 43
 
< 0.1%
0.000225 4035
0.1%
0.000256 1070
 
< 0.1%
0.000324 6
 
< 0.1%
0.000361 3210
0.1%
ValueCountFrequency (%)
0.37161216 67
 
< 0.1%
0.31225744 46
 
< 0.1%
0.258064 2342
< 0.1%
0.25 102
 
< 0.1%
0.20903184 781
 
< 0.1%
0.16516096 4023
0.1%
0.16 561
 
< 0.1%
0.12645136 437
 
< 0.1%
0.126025 108
 
< 0.1%
0.1225 372
 
< 0.1%

Length2
Real number (ℝ)

Distinct123379
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1163.4679
Minimum2.5 × 10-5
Maximum11142.702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 MiB
2023-02-18T22:52:28.140486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.5 × 10-5
5-th percentile1.004004
Q113.373649
median140.11457
Q31230.4661
95-th percentile6300.9463
Maximum11142.702
Range11142.702
Interquartile range (IQR)1217.0924

Descriptive statistics

Standard deviation2126.2558
Coefficient of variation (CV)1.8275156
Kurtosis5.8583692
Mean1163.4679
Median Absolute Deviation (MAD)138.43495
Skewness2.4614034
Sum6.3366315 × 109
Variance4520963.7
MonotonicityNot monotonic
2023-02-18T22:52:28.256442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 26243
 
0.5%
1 25048
 
0.5%
0.25 18213
 
0.3%
2.25 17758
 
0.3%
1.004004 16856
 
0.3%
1.006009 10146
 
0.2%
1.002001 9486
 
0.2%
9 8566
 
0.2%
4.016016 8540
 
0.2%
1.44 8115
 
0.1%
Other values (123369) 5297360
97.3%
ValueCountFrequency (%)
2.5 × 10-512
 
< 0.1%
3.6 × 10-519
 
< 0.1%
4.9 × 10-514
 
< 0.1%
6.4 × 10-520
 
< 0.1%
8.1 × 10-519
 
< 0.1%
0.0001 61
< 0.1%
0.000121 25
< 0.1%
0.000144 12
 
< 0.1%
0.000169 31
< 0.1%
0.000196 8
 
< 0.1%
ValueCountFrequency (%)
11142.70248 5
 
< 0.1%
11142.28025 15
< 0.1%
11142.06914 12
< 0.1%
11141.64692 10
< 0.1%
11141.43581 16
< 0.1%
11141.2247 10
< 0.1%
11140.8025 5
 
< 0.1%
11140.5914 19
< 0.1%
11140.3803 3
 
< 0.1%
11140.16921 5
 
< 0.1%

Pressure2
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.491531
Minimum0.000625
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 MiB
2023-02-18T22:52:28.353394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.000625
5-th percentile0.000625
Q10.01
median0.0225
Q316
95-th percentile25
Maximum256
Range255.99937
Interquartile range (IQR)15.99

Descriptive statistics

Standard deviation53.125639
Coefficient of variation (CV)3.0372207
Kurtosis15.664085
Mean17.491531
Median Absolute Deviation (MAD)0.021875
Skewness4.1424603
Sum95264665
Variance2822.3335
MonotonicityNot monotonic
2023-02-18T22:52:28.438326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
16 1599962
29.4%
0.0225 1539205
28.3%
0.000625 1038140
19.1%
0.01 562015
 
10.3%
256 249621
 
4.6%
25 158730
 
2.9%
0.16 143759
 
2.6%
2.89 111774
 
2.1%
0.0025 19978
 
0.4%
4 11814
 
0.2%
Other values (2) 11333
 
0.2%
ValueCountFrequency (%)
0.000625 1038140
19.1%
0.0025 19978
 
0.4%
0.01 562015
 
10.3%
0.0225 1539205
28.3%
0.16 143759
 
2.6%
2.89 111774
 
2.1%
4 11814
 
0.2%
16 1599962
29.4%
25 158730
 
2.9%
100 6185
 
0.1%
ValueCountFrequency (%)
256 249621
 
4.6%
144 5148
 
0.1%
100 6185
 
0.1%
25 158730
 
2.9%
16 1599962
29.4%
4 11814
 
0.2%
2.89 111774
 
2.1%
0.16 143759
 
2.6%
0.0225 1539205
28.3%
0.01 562015
 
10.3%

Average year Humidity (%)
Real number (ℝ)

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.67414297
Minimum0.545
Maximum0.80416667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 MiB
2023-02-18T22:52:28.535132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.545
5-th percentile0.545
Q10.61833333
median0.66833333
Q30.75666667
95-th percentile0.79333333
Maximum0.80416667
Range0.25916667
Interquartile range (IQR)0.13833333

Descriptive statistics

Standard deviation0.081146398
Coefficient of variation (CV)0.12036972
Kurtosis-1.2441021
Mean0.67414297
Median Absolute Deviation (MAD)0.088333333
Skewness-0.20311737
Sum3671605.8
Variance0.006584738
MonotonicityNot monotonic
2023-02-18T22:52:28.647341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.7566666667 1496607
27.5%
0.6441666667 536570
 
9.9%
0.545 513519
 
9.4%
0.715 314906
 
5.8%
0.6666666667 308605
 
5.7%
0.6683333333 232779
 
4.3%
0.8041666667 194965
 
3.6%
0.58 149988
 
2.8%
0.7933333333 147657
 
2.7%
0.625 146197
 
2.7%
Other values (27) 1404538
25.8%
ValueCountFrequency (%)
0.545 513519
9.4%
0.5458333333 96932
 
1.8%
0.555 75528
 
1.4%
0.5566666667 53897
 
1.0%
0.5583333333 55599
 
1.0%
0.565 138476
 
2.5%
0.5725 15921
 
0.3%
0.5741666667 68208
 
1.3%
0.58 149988
 
2.8%
0.585 22313
 
0.4%
ValueCountFrequency (%)
0.8041666667 194965
 
3.6%
0.7933333333 147657
 
2.7%
0.7675 29521
 
0.5%
0.7566666667 1496607
27.5%
0.7166666667 76882
 
1.4%
0.715 314906
 
5.8%
0.705 65058
 
1.2%
0.7033333333 98573
 
1.8%
0.6866666667 115543
 
2.1%
0.6683333333 232779
 
4.3%

Yearly Sun Hours (hours)
Real number (ℝ)

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.18109
Minimum73.3
Maximum121.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 MiB
2023-02-18T22:52:28.757455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum73.3
5-th percentile83.8
Q1104.6
median104.6
Q3111.4
95-th percentile113.8
Maximum121.2
Range47.9
Interquartile range (IQR)6.8

Descriptive statistics

Standard deviation9.4874531
Coefficient of variation (CV)0.090201127
Kurtosis2.2501069
Mean105.18109
Median Absolute Deviation (MAD)5.8
Skewness-1.4841031
Sum5.7285105 × 108
Variance90.011767
MonotonicityNot monotonic
2023-02-18T22:52:28.863723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
104.6 1496607
27.5%
113.2 536570
 
9.9%
109.7 513519
 
9.4%
111.4 376813
 
6.9%
99.8 314906
 
5.8%
119.3 232779
 
4.3%
75.9 160838
 
3.0%
112.7 149988
 
2.8%
83.8 147657
 
2.7%
98.6 146197
 
2.7%
Other values (27) 1370457
25.2%
ValueCountFrequency (%)
73.3 34127
 
0.6%
75.9 160838
3.0%
79.8 29521
 
0.5%
83.8 147657
2.7%
85.2 76882
1.4%
87.9 65058
1.2%
95.2 85594
1.6%
95.7 33899
 
0.6%
96.7 20298
 
0.4%
97.3 13959
 
0.3%
ValueCountFrequency (%)
121.2 12230
 
0.2%
119.3 232779
4.3%
114.2 15921
 
0.3%
113.8 29278
 
0.5%
113.2 536570
9.9%
113 115543
 
2.1%
112.7 149988
 
2.8%
112.4 136707
 
2.5%
112.3 32551
 
0.6%
111.9 22313
 
0.4%
Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean365.8349
Minimum8.6244906
Maximum844.63536
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 MiB
2023-02-18T22:52:28.973412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8.6244906
5-th percentile24.985868
Q188.244424
median239.83298
Q3738.89129
95-th percentile844.63536
Maximum844.63536
Range836.01087
Interquartile range (IQR)650.64687

Descriptive statistics

Standard deviation315.6559
Coefficient of variation (CV)0.86283703
Kurtosis-1.615061
Mean365.8349
Median Absolute Deviation (MAD)203.80628
Skewness0.41679478
Sum1.9924579 × 109
Variance99638.646
MonotonicityNot monotonic
2023-02-18T22:52:29.077706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
738.8912904 1496607
27.5%
239.8329786 536570
 
9.9%
844.6353557 513519
 
9.4%
132.3046201 314906
 
5.8%
129.6052047 308605
 
5.7%
323.1713942 232779
 
4.3%
141.108805 160838
 
3.0%
138.9440724 149988
 
2.8%
210.3711615 147657
 
2.7%
64.19839704 146197
 
2.7%
Other values (28) 1438665
26.4%
ValueCountFrequency (%)
8.624490588 13959
 
0.3%
9.060436221 1556
 
< 0.1%
11.44334889 18610
 
0.3%
14.24194167 5378
 
0.1%
16.15263706 35306
 
0.6%
19.58559006 4
 
< 0.1%
19.91070541 33899
 
0.6%
21.4503848 53897
1.0%
22.17890173 20298
 
0.4%
24.98586786 96932
1.8%
ValueCountFrequency (%)
844.6353557 513519
 
9.4%
738.8912904 1496607
27.5%
323.1713942 232779
 
4.3%
239.8329786 536570
 
9.9%
230.7582809 98573
 
1.8%
210.3711615 147657
 
2.7%
167.2108871 29278
 
0.5%
141.108805 160838
 
3.0%
138.9440724 149988
 
2.8%
132.3046201 314906
 
5.8%

Interactions

2023-02-18T22:51:45.323686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:47:11.702361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:47:28.973782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:47:45.046796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:01.536773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:17.803643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:34.166439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:50.259433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:06.639827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:22.824619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:38.594522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:54.433571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:10.404620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:26.708327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:42.384758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:58.052623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:13.605495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:29.510547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:46.170871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:47:13.043616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:47:29.828653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:47:45.934469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:02.438719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:18.717325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:35.023397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:51.138445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:07.529957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:23.671601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:39.502282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:55.299575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:11.272705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:27.557004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:43.238629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:58.914508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:14.472806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:30.371525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:47.042644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-02-18T22:48:13.283868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:29.657360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:45.775046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:01.869709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:18.333936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:34.248336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:50.056449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:06.022901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:22.268737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:38.051488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:53.741174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:09.323813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:25.143793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:41.017552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:57.818452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:47:25.394520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:47:41.445412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:47:57.941720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:14.193603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:30.567513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:46.706592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:02.765707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:19.211733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:35.100383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:50.934495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:06.917369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:23.205220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:38.918395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:54.581640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:10.162596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:26.013622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:41.893580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:58.697481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:47:26.293481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:47:42.354271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:47:58.858573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:15.073470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:31.462549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:47.573920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:03.656522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:20.129755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:35.969815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:51.804540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:07.798604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:24.096677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:39.782757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:55.471650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:11.010488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:26.843854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:42.751508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:59.550463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:47:27.176449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:47:43.238931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:47:59.746485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:16.018707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:32.389518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:48.482503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:04.824806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:21.018796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:36.848484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:52.701580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:08.648817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:24.970728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:40.643633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:56.327019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:11.887500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:27.739663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:43.596564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:52:00.388435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:47:28.071931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:47:44.137186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:00.680641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:16.904366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:33.291799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:48:49.354784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:05.704326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:21.937660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:37.697351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:49:53.557701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:09.554832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:25.824810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:41.509691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:50:57.209124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:12.772762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:28.610328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T22:51:44.449527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-18T22:52:29.204555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
PipeIdNo_InspectionsInspectionYearpreventive_maintenance_rateAge_pipe_at_inspectionAverage_MonthsLastRevMonthsLastRevNumConnectionsRelative_Thicknesspipe_areaarea_connectionincidence_areaDiameter2Length2Pressure2Average year Humidity (%)Yearly Sun Hours (hours)Population density (persons/sqkm)No_Incidentspipe_inspected_frequentlyIncidenceNumConnectionsUnderconnection_boolgas_naturalMaterial_Acrylonitrile-Butadiene-StyreneMaterial_CopperMaterial_Fiberglass-Reinforced PlasticMaterial_PolyethyleneMaterial_Polypropylene
PipeId1.0000.242-0.115-0.021-0.2210.0790.078-0.0560.0170.019-0.062-0.0170.106-0.0150.0330.0420.069-0.0240.0200.1430.0270.0070.0970.2910.0280.1010.0840.0590.164
No_Inspections0.2421.000-0.1600.0140.316-0.212-0.1440.0530.1420.1640.029-0.0100.1510.115-0.115-0.1340.1050.1480.0180.1550.0320.0110.0680.5190.0520.1610.0630.0720.246
InspectionYear-0.115-0.1601.0000.0050.252-0.0310.2240.011-0.043-0.0000.016-0.000-0.0570.0200.031-0.009-0.008-0.0230.0030.0600.0060.0030.0110.1660.0480.0510.0170.0420.082
preventive_maintenance_rate-0.0210.0140.0051.0000.039-0.033-0.0360.0830.0220.0490.0640.4950.0050.053-0.029-0.0360.0090.0570.6270.0120.5680.0000.0870.1190.0030.0670.0490.0310.008
Age_pipe_at_inspection-0.2210.3160.2520.0391.0000.1830.1500.0720.2090.0730.0480.0060.1090.036-0.2420.078-0.0940.2340.0310.0740.0290.0070.1040.2700.0530.1010.2410.1230.164
Average_MonthsLastRev0.079-0.212-0.031-0.0330.1831.0000.559-0.0210.088-0.044-0.023-0.0180.040-0.057-0.1010.314-0.106-0.0230.0180.4030.0310.0040.0420.1190.0190.0690.0260.0580.182
MonthsLastRev0.078-0.1440.224-0.0360.1500.5591.000-0.0100.066-0.018-0.013-0.0220.027-0.025-0.0720.306-0.060-0.0880.0200.2940.0350.0040.0260.1180.0330.0580.0460.0520.100
NumConnections-0.0560.0530.0110.0830.072-0.021-0.0101.0000.0430.4600.9420.038-0.1710.538-0.1210.017-0.0090.0430.0510.0030.0470.0110.5170.0290.0570.0170.0130.0420.009
Relative_Thickness0.0170.142-0.0430.0220.2090.0880.0660.0431.0000.171-0.0260.0040.638-0.001-0.9410.0380.2130.2350.0340.0870.0250.0020.0970.0660.1180.0240.4380.1810.019
pipe_area0.0190.164-0.0000.0490.073-0.044-0.0180.4600.1711.0000.2880.0180.2840.955-0.099-0.0220.0520.0210.0330.0240.0250.0010.2960.0460.0690.0190.0460.0750.011
area_connection-0.0620.0290.0160.0640.048-0.023-0.0130.942-0.0260.2881.0000.031-0.2650.384-0.0780.014-0.0280.0370.0000.0020.0000.0000.0000.0030.0000.0110.0000.0010.000
incidence_area-0.017-0.010-0.0000.4950.006-0.018-0.0220.0380.0040.0180.0311.000-0.0060.024-0.009-0.0170.0040.0240.0240.0000.0950.0000.0000.0160.0000.0250.0000.0050.002
Diameter20.1060.151-0.0570.0050.1090.0400.027-0.1710.6380.284-0.265-0.0061.0000.007-0.3810.0040.1590.1380.0140.0200.0090.0000.0700.0510.1790.0190.2030.2230.019
Length2-0.0150.1150.0200.0530.036-0.057-0.0250.538-0.0010.9550.3840.0240.0071.0000.000-0.0230.011-0.0220.0300.0320.0280.0050.4700.0110.0180.0020.0220.0260.003
Pressure20.033-0.1150.031-0.029-0.242-0.101-0.072-0.121-0.941-0.099-0.078-0.009-0.3810.0001.000-0.057-0.178-0.2530.0040.0210.0040.0020.1020.0300.8820.0110.0320.7230.013
Average year Humidity (%)0.042-0.134-0.009-0.0360.0780.3140.3060.0170.038-0.0220.014-0.0170.004-0.023-0.0571.000-0.5300.2340.0310.1980.0280.0050.0580.0920.0800.0360.1260.1120.042
Yearly Sun Hours (hours)0.0690.105-0.0080.009-0.094-0.106-0.060-0.0090.2130.052-0.0280.0040.1590.011-0.178-0.5301.0000.0970.0240.1950.0220.0060.0590.0830.0600.0310.1190.0920.023
Population density (persons/sqkm)-0.0240.148-0.0230.0570.234-0.023-0.0880.0430.2350.0210.0370.0240.138-0.022-0.2530.2340.0971.0000.0440.1800.0390.0060.0560.0770.0640.0310.1740.1320.024
No_Incidents0.0200.0180.0030.6270.0310.0180.0200.0510.0340.0330.0000.0240.0140.0300.0040.0310.0240.0441.0000.0090.5040.0000.0870.0370.0030.0280.0440.0290.002
pipe_inspected_frequently0.1430.1550.0600.0120.0740.4030.2940.0030.0870.0240.0020.0000.0200.0320.0210.1980.1950.1800.0091.0000.0050.0010.0070.0380.0260.0130.0120.0350.019
Incidence0.0270.0320.0060.5680.0290.0310.0350.0470.0250.0250.0000.0950.0090.0280.0040.0280.0220.0390.5040.0051.0000.0000.0400.0430.0020.0270.0180.0160.000
NumConnectionsUnder0.0070.0110.0030.0000.0070.0040.0040.0110.0020.0010.0000.0000.0000.0050.0020.0050.0060.0060.0000.0010.0001.0000.0070.0030.0030.0080.0000.0010.012
connection_bool0.0970.0680.0110.0870.1040.0420.0260.5170.0970.2960.0000.0000.0700.4700.1020.0580.0590.0560.0870.0070.0400.0071.0000.0280.1080.0130.0010.0900.011
gas_natural0.2910.5190.1660.1190.2700.1190.1180.0290.0660.0460.0030.0160.0510.0110.0300.0920.0830.0770.0370.0380.0430.0030.0281.0000.0190.2670.0190.0380.001
Material_Acrylonitrile-Butadiene-Styrene0.0280.0520.0480.0030.0530.0190.0330.0570.1180.0690.0000.0000.1790.0180.8820.0800.0600.0640.0030.0260.0020.0030.1080.0191.0000.0120.0360.8190.015
Material_Copper0.1010.1610.0510.0670.1010.0690.0580.0170.0240.0190.0110.0250.0190.0020.0110.0360.0310.0310.0280.0130.0270.0080.0130.2670.0121.0000.0070.1580.003
Material_Fiberglass-Reinforced Plastic0.0840.0630.0170.0490.2410.0260.0460.0130.4380.0460.0000.0000.2030.0220.0320.1260.1190.1740.0440.0120.0180.0000.0010.0190.0360.0071.0000.4660.008
Material_Polyethylene0.0590.0720.0420.0310.1230.0580.0520.0420.1810.0750.0010.0050.2230.0260.7230.1120.0920.1320.0290.0350.0160.0010.0900.0380.8190.1580.4661.0000.192
Material_Polypropylene0.1640.2460.0820.0080.1640.1820.1000.0090.0190.0110.0000.0020.0190.0030.0130.0420.0230.0240.0020.0190.0000.0120.0110.0010.0150.0030.0080.1921.000

Missing values

2023-02-18T22:52:00.764210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-18T22:52:06.501663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PipeIdNo_InspectionsNo_IncidentsInspectionYearpreventive_maintenance_rateAge_pipe_at_inspectionpipe_inspected_frequentlyAverage_MonthsLastRevMonthsLastRevIncidenceNumConnectionsNumConnectionsUnderRelative_Thicknesspipe_areaarea_connectionincidence_areaconnection_boolgas_naturalMaterial_Acrylonitrile-Butadiene-StyreneMaterial_CopperMaterial_Fiberglass-Reinforced PlasticMaterial_PolyethyleneMaterial_PolypropyleneDiameter2Length2Pressure2Average year Humidity (%)Yearly Sun Hours (hours)Population density (persons/sqkm)
048961650.002013.000.001.00121.4016.000.00000.161.540.000.0001000100.0060.720.160.55107.5024.99
148961650.002015.000.003.00121.4022.000.00000.161.540.000.0001000100.0060.720.160.55107.5024.99
248961650.002016.000.004.00121.4022.000.00000.161.540.000.0001000100.0060.720.160.55107.5024.99
348961650.002018.000.006.00121.4023.000.00000.161.540.000.0001000100.0060.720.160.55107.5024.99
448961650.002020.000.008.00121.4024.000.00000.161.540.000.0001000100.0060.720.160.55107.5024.99
548964550.002013.000.001.00121.4016.000.00000.220.590.000.0001000100.014.330.160.55107.5024.99
648964550.002015.000.003.00121.4022.000.00000.220.590.000.0001000100.014.330.160.55107.5024.99
748964550.002016.000.004.00121.4022.000.00000.220.590.000.0001000100.014.330.160.55107.5024.99
848964550.002018.000.006.00121.4023.000.00000.220.590.000.0001000100.014.330.160.55107.5024.99
948964550.002020.000.008.00121.4024.000.00000.220.590.000.0001000100.014.330.160.55107.5024.99
PipeIdNo_InspectionsNo_IncidentsInspectionYearpreventive_maintenance_rateAge_pipe_at_inspectionpipe_inspected_frequentlyAverage_MonthsLastRevMonthsLastRevIncidenceNumConnectionsNumConnectionsUnderRelative_Thicknesspipe_areaarea_connectionincidence_areaconnection_boolgas_naturalMaterial_Acrylonitrile-Butadiene-StyreneMaterial_CopperMaterial_Fiberglass-Reinforced PlasticMaterial_PolyethyleneMaterial_PolypropyleneDiameter2Length2Pressure2Average year Humidity (%)Yearly Sun Hours (hours)Population density (persons/sqkm)
544632141279464810.002014.000.003.00124.0024.000.00000.0219.260.000.0001000100.009467.2916.000.57108.4045.79
544632241279474010.002014.000.003.00124.0024.000.00000.0221.560.000.0001000100.015816.8116.000.57108.4045.79
544632341279485610.002014.000.003.00124.0024.000.00000.0220.500.000.0001000100.0010730.8916.000.57108.4045.79
544632441279504810.002014.000.003.00124.0024.000.00000.030.420.000.0001000100.011.4616.000.57108.4045.79
544632541279521510.002014.000.003.00124.0024.000.00000.0218.770.000.0001000100.014406.5716.000.57108.4045.79
544632641279551210.002014.000.003.00124.0024.000.00000.020.550.000.0001000100.007.7016.000.57108.4045.79
544632741279624310.002014.000.003.00124.0024.000.00000.022.090.000.0001000100.0154.8316.000.57108.4045.79
544632841279626110.002014.000.003.00124.0024.000.00000.022.330.000.0001000100.0168.0516.000.57108.4045.79
544632941279628810.002014.000.003.00124.0024.000.00000.022.680.000.0001000100.0190.0816.000.57108.4045.79
544633041279652910.002014.000.003.00124.0024.000.00000.021.120.000.0001000100.0115.8216.000.57108.4045.79